Toll‐like receptors and NLRP3 as central regulators of pancreatic islet inflammation in type 2 diabetes
Bibliographic record
Abstract
The global health and economic burden of type 2 diabetes (T2D) has reached staggering proportions. Current projections estimate that 592 million people will have diabetes by 2035. T2D-which comprises 90% of cases-is a complex disease, in most cases resulting from a combination of predisposing genes and an unhealthy environment. Clinical onset of the disease occurs when pancreatic β cells fail in the face of insulin resistance. It has long been appreciated that chronic activation of the innate immune system is associated with T2D, and many organs critical to the regulation of glucose homeostasis show signs of a chronic inflammatory process, including the pancreatic islets of Langerhans. Recent clinical trials using IL-1-targeting agents have confirmed that inflammation contributes to β-cell failure in humans with T2D. However, little is known about the nature of the pro-inflammatory response within the islet, and there is considerable debate about the triggers for islet inflammation, which may be systemically derived and/or tissue-specific. In this review, we present evidence that Toll-like receptors 2 and 4 and the NLRP3 (Nucleotide-binding oligomerization domain, Leucine-rich Repeat and Pyrin domain containing 3) inflammasome are triggers for islet inflammation in T2D and propose that the activation of macrophages by these triggers mediates islet endocrine cell dysfunction. Therapeutically targeting these receptors may improve hyperglycemia and protect the β cell in T2D.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".